Margin distribution and structural diversity guided ensemble pruning
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Learning sample-aware threshold for semi-supervised learning
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Communication-efficient clustered federated learning via model distance
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On-the-fly image-level oversampling for imbalanced datasets of manufacturing defects
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Understanding imbalanced data: XAI & interpretable ML framework
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PANACEA: a neural model ensemble for cyber-threat detection
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Hierarchical U-net with re-parameterization technique for spatio-temporal weather forecasting
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Compositional scene modeling with global object-centric representations
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Tracking treatment effect heterogeneity in evolving environments
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Explaining neural networks without access to training data
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Nrat: towards adversarial training with inherent label noise
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Style spectroscope: improve interpretability and controllability through Fourier analysis
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Better schedules for low precision training of deep neural networks
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Exploiting counter-examples for active learning with partial labels
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Learning de-biased regression trees and forests from complex samples
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Task-decoupled interactive embedding network for object detection
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GS2P: a generative pre-trained learning to rank model with over-parameterization for web-scale search
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Entity recognition based on heterogeneous graph reasoning of visual region and text candidate
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Sanitized clustering against confounding bias
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Scalable variable selection for two-view learning tasks with projection operators
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Chinese character recognition with radical-structured stroke trees
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Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation
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Hybrid acceleration techniques for the physics-informed neural networks: a comparative analysis
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Neural network structure simplification by assessing evolution in node weight magnitude
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Multi-label image classification with multi-layered multi-perspective dynamic semantic representation
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Applied machine learning to the determination of biochar hydrogen sulfide adsorption capacity
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Online binary classification from similar and dissimilar data
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Smoclust: synthetic minority oversampling based on stream clustering for evolving data streams
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Knowledge-aware image understanding with multi-level visual representation enhancement for visual question answering
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Understanding transfer learning and gradient-based meta-learning techniques
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Solving imbalanced learning with outlier detection and features reduction
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Learning to bid and rank together in recommendation systems
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Libsignal: an open library for traffic signal control
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Deep reinforcement learning for multi-class imbalanced training: applications in healthcare
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Multi-agent reinforcement learning for fast-timescale demand response of residential loads
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GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data
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Cost-sensitive sparse group online learning for imbalanced data streams
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Composite score for anomaly detection in imbalanced real-world industrial dataset
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GVFs in the real world: making predictions online for water treatment
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Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning
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Fair tree classifier using strong demographic parity
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Persian offensive language detection
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Consolidated learning: a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks
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Fraud detection with natural language processing
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A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
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A survey of class-imbalanced semi-supervised learning
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Federated learning with superquantile aggregation for heterogeneous data
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On the incompatibility of accuracy and equal opportunity
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